In-class practice

Time series data

library(tidyverse)

data(nasa, package="GGally")

Choose two locations in x and two locations in y

nasa %>% filter(x %in% c(1, 2), y %in% c(1, 10)) %>% 
  mutate(y = factor(y), x = factor(x)) %>%
  ggplot(aes(x = time, y = temperature, group = id, color = y)) + 
  geom_line(aes(linetype = x))

1. For one location, draw a time line of Ozone over the time frame (time).

nasa %>% filter(x == 1, y == 1) %>%
  ggplot(aes(x = date, y = ozone)) + geom_line()

2. Plot separate lines for each of the years, i.e. put month on the x-axis and ozone on the y-axis for the same location. Is there a seasonal pattern apparent?

library(lubridate)

## Loading required package: timechange

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

nasa %>% filter(x == 1, y == 1) %>% 
  ggplot(aes(x = month(date, label = TRUE), y = ozone, group = year, color = factor(year))) + geom_line()

3. Pick a location with x in 1:10 and y in 7:10. Plot temperature over time.

nasa %>% filter(x %in% c(1 : 10), y %in% c(7 : 10)) %>% 
  mutate(y = factor(y), x = factor(x)) %>%
  ggplot(aes(x = time, y = temperature, group = id, color = x)) + 
  geom_line() + facet_wrap(~y)

4. Use gather() function to put different variables versus time

nasa %>% gather(key = variable, value = value, 7 : 13) %>%
  filter(x == 2, y == 1) %>% 
  ggplot(aes(x = month(date, label = TRUE), y = value, group = year, color = year)) + 
  geom_line() + facet_wrap(~variable, scales = "free_y")

Box data

1. For each movie and distributor, find the (1) highest total gross, (2) the last date (and week) the movie was shown in theaters, (3) the gross the movie made in the first week it was released.

data(box, package="classdata")

box_summary = box %>% group_by(Movie, Distributor) %>%
  summarise(Gross.final.1 = max(Total.Gross),
            Gross.final.2 = Total.Gross[which.max(Week)],
            Gross.initial.1 = min(Total.Gross),
            Gross.initial.2 = Total.Gross[which.min(Week)],
            Max.Thtrs = max(Thtrs.),
            date.last = max(Date),
            week.count = max(Week)) %>%
  mutate(Gross.final.diff = Gross.final.1 - Gross.final.2,
         Gross.initial.diff = Gross.initial.1 - Gross.initial.2)

## `summarise()` has grouped output by 'Movie', 'Distributor'. You can override
## using the `.groups` argument.

box_summary1 = box_summary %>% filter(Gross.final.diff == 0,
                                      Gross.initial.diff == 0,
                                      Max.Thtrs > 100,
                                      week.count < 30)

name.clean = box_summary1$Movie

box1 = box %>% filter(Movie %in% name.clean)

box_summary2 = box_summary1 %>% arrange(desc(Gross.final.1))
box_summary2 = box_summary2[1 : 3, ]

box1 %>% ggplot(aes(x = Date, y = Total.Gross, 
                    group = interaction(Movie, Distributor))) + geom_line() +
  geom_text(aes(x = date.last - 500, y = Gross.final.1, label = Movie, color = Movie), 
            data = box_summary2)

box1 %>% ggplot(aes(x = Date, y = Total.Gross, 
                    group = interaction(Movie, Distributor))) + geom_line() +
  geom_text(aes(x = date.last, y = Gross.final.1, label = Movie), 
            data = box_summary1 %>% filter(Gross.final.1 >= 368049635) )

Non-overlapping text:

box1 %>% ggplot(aes(x = Date, y = Total.Gross, 
                    group = interaction(Movie, Distributor))) + geom_line() +
  ggrepel::geom_text_repel(aes(x = date.last, y = Gross.final.1, label = Movie), 
            data = box_summary1 %>% filter(Gross.final.1 >= 368049635),
            colour="grey50")

Layers

nasa.1 = nasa %>% filter(id == "1-10") %>% 
  filter(temperature == max(temperature))

nasa %>% filter(id == "1-1") %>% 
  ggplot(aes(x = time, y= temperature)) + geom_point() +
  geom_smooth(method="loess", span = 0.25) + 
  geom_point(data = nasa %>% filter(id == "1-10"), 
             aes(x = time, y= temperature), 
             shape = 2, col = 2) + 
  geom_smooth(data = nasa %>% filter(id == "1-10"), 
             aes(x = time, y= temperature), 
             method="loess", span = 0.25, col = 2) + 
  geom_text(data = nasa.1, aes(x = time, y = temperature + 0.5, label = temperature), col = "grey50", size = 2.5)

nasa.2 = nasa %>% filter(id == "1-1") %>% 
  mutate(season = "winter") %>%
  mutate(season = replace(season, month %in% c(6, 7, 8), "summer")) %>%
  mutate(season = replace(season, month %in% c(3, 4, 5), "spring")) %>%
  mutate(season = replace(season, month %in% c(9, 10, 11), "fall"))

nasa %>% filter(id == "1-1") %>%
  ggplot(aes(x = time, y= temperature)) + geom_point() +
  geom_smooth(method="loess", span = 0.25) +
  geom_text(aes(x = time, y = temperature + 0.2, label = temperature, colour = season), 
            data = nasa.2, size = 2.5)

nasa %>% filter(id %in% c("1-1", "1-10")) %>%
  ggplot(aes(x = time, y= temperature, group = id)) + geom_point(aes(colour=id)) +
  geom_smooth(method="loess", span = 0.25) +
  geom_text(aes(x = time, y= temperature, label=id), 
            data = nasa %>% filter(id %in% c("1-1", "1-10"), time == 50))

Map

library(maps)

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

states = map_data("state")

states %>% ggplot(aes(x = long, y = lat)) + geom_point()

states %>% ggplot(aes(x = long, y = lat)) + geom_path(aes(group = group))